Systems and methods for evaluating objects within an ultrasound image

ABSTRACT

According to the invention, an object in an ultrasound image is characterized by considering various in-vivo object parameters and their variability within the ultrasonic imaging data. Specifically, it is assumed that the object may be defined in terms of statistical properties (or object identifying parameters), which are consistently different from properties of the environment. Such properties are referred to as the object&#39;s signature. The statistical properties are calculated at selected locations within the image to determine if they fall within a predetermined range of values which represents the object. If within the range, the locations are marked to indicate they are positioned within the object. A border may then be drawn around the object and the area calculated.

This application is a continuation of Ser. No. 09/165,670 filed Oct. 2,1998.

BACKGROUND OF THE INVENTION

This invention relates to the automated characterization andidentification of objects, including automated detection of theirborders, in intravascular ultrasonic imaging.

The value of ultrasonic imaging can be enhanced if models can bedeveloped which accurately correlate properties of ultrasound objects inan in-vivo environment. Heretofore there have been few automatedapproaches in the field of in-vivo ultrasonic object definition andidentification. Previously proposed approaches may be classified in twocategories. First, the defining of an object as an area surrounded by adetected border. Detection of the border in turn is based on localproperties and behavior of the border. Second, the development of atheoretical model for an ultrasound object which is validated for invitro studies.

According to the first category, approaches have been developed at theThoraxcenter in Rotterdam, Holland, and at the University of Iowa whichemploy feature extraction techniques for border detection. In thoseapproaches an object is defined as the area encompassed by a detectedborder, and the algorithms used are optimized to provide the bestpossible border. These approaches are limited because algorithms providelittle information about the parameters characterizing the object underobservation. Neither can the algorithms adapt their behavior inaccordance with frame-to-frame variants in object properties. Inaddition, the algorithms are computational and time intensive incross-sectional area computation, since they must completely calculatethe object border in each frame of the volume.

In the second category of approaches, tissue modeling techniques havebeen developed for comparing data patterns with predefined models, e.g.,at the Stanford Center for Cardiac Interventions and the University ofTexas. In these types of techniques, a consistent tissue behavior isassumed which can be modeled. The models describe internal properties ofan object which can be used to identify the object. However, such modelsare inherently limited in that by their nature they cannot accommodatevariations in object properties from patient to patient, or even fromframe to frame. A paper by Petropulu et al. entitled MODELING THEULTRASOUND BACKSCATTERED SIGNAL USING α-STABLE DISTRIBUTIONS, 1996 IEEEUltrasonics Symposium, p. 103 is representative of the model-basedapproach. Therein certain assumptions about theoretical statisticalbehavior are made, and the assumptions are used to identify the objectin an in-vivo case study. This limited approach is subject tosignificant errors because it yields a model which only partiallydescribes the object behavior and does not take into account variationsfrom case to case.

Most known techniques for object border detection use a purely manualmethod for border tracing, which is done simply by drawing the boundaryof the object. This procedure is slow and is subject to errors andvariations between users. Moreover, it does not allow for thecharacterization of the object within the border.

One known description of a combination of different approaches isSpencer et al., CHARACTERISATION OF ATHEROSCLEROTIC PLAQUE BY SPECTRALANALYSIS OF 30 MHZ INTRAVASCULAR ULTRASOUND RADIO FREQUENCY DATA, 1996IEEE ULTRASONICS SYMPOSIUM, p. 1073, wherein a statistical model isdeveloped from in-vitro studies, then applied to in-vivo cases. Such anapproach is limited by both the differences between in-vitro and in-vivoconditions and between in-vivo cases.

What are needed are better techniques for border detection and foridentifying and characterizing objects and features of ultrasonicimaging.

SUMMARY OF THE INVENTION

The invention provides exemplary systems and methods for evaluatingobjects located within ultrasonic images. According to one exemplarymethod, in-vivo ultrasound image data is obtained and an image isconstructed from the data which includes at least one object. At leasttwo parameters are calculated from the data for selected locationswithin the object. These parameters are representative of the intensityof the object and the spacial structure of the object.

Preferably, the data that is collected is time-domain data. This data istransformed into frequency-domain data and compressed. The twoparameters preferably comprise the zero frequency magnitude of thecompressed frequency-domain data and the sum of the frequency magnitudesof the compressed frequency-domain data. Use of these two parameters isparticularly advantageous in that they may be used to characterize aphysical object within a patient. For example, the zero frequencymagnitude of the compressed frequency-domain data is representative ofthe physical composition of the physical object, e.g., its hardness, andthe sum of the frequency magnitudes of the compressed frequency-domaindata is representative of the structure of the physical object. Hence,the invention provides a way to obtain patient specific parameters in ain-vivo processes. Further, these parameters represent various physicalcharacteristic of the object under evaluation so that a treatment maymore carefully be tailored. Moreover, these parameters may be saved andkept as part of the patient's history so that they may be compared toparameters calculated after one or more treatments of the object.

In another exemplary method, in-vivo ultrasound image data is providedin a plurality of frames. An object is identified within each image bymoving a region of interest to different locations in the image andevaluating object identifying parameters at the different locations todetermine if the parameters fall within an acceptable range that areindicative of the object. The area of the object within each of theframes is then computed based on the area of the locations having theparameters which fall within the acceptable range. The areas of twoadjacent frames are then compared to determine if the difference betweenthe two areas exceeds a predetermined amount. If so, the area of one ofthe adjacent frames is recomputed using different criteria.

For example, the range of acceptable object identifying parameters maybe varied when recomputing the area of one of the adjacent frames. Asanother example, a starting location of the region of interest may bevaried when recomputing the area of one of the adjacent frames. As stillanother example, the size of the region of interest may be varied whenrecomputing the area of one of the adjacent frames. In the event thatthe difference between recomputed area and the area of the object in theadjacent frame still exceeds the predetermined amount, a message may beproduced indicating the discrepancy.

In one specific embodiment, a method is provided for evaluating anobject within an ultrasound image that is constructed from time-domaindata. According to the method, a region of interest within the object isselected for observation. At the selected region of interest, atransformation of the time-domain data is performed to obtainfrequency-domain data. The frequency-domain data is then compressed orfiltered, and object identifying parameters are obtained from thecompressed frequency-domain data. Multiple definition regions ofinterest which are subsets of the selected region of interest are thendefined. Preferably, the definition regions of interest are proportionalin shape to the selected region of interest and are located at adistinct locations within the selected region of interest. Atransformation of the time-domain data defining the definition regionsof interest is then performed to obtain frequency-domain data that isrepresentative of the definition regions of interest. From this data, arange of acceptable object identifying parameters is obtained.

Once this range has been determined, definition regions of interest arepositioned at selected locations in the ultrasound image, andtransformations of the time-domain data are performed to obtainfrequency-domain data representative of the definition regions ofinterest in the ultrasound image. Object identifying parameters fromthis frequency-domain data are then obtained. These object identifyingparameters are then evaluated to determine if they are within the rangeof acceptable object identifying parameters that was previouslycalculated. The selected definition regions of interest in theultrasound image which have object identifying parameters which fallwithin the acceptable range are then marked or flagged so that an objectboundary may be constructed around the flagged definition regions ofinterest. Once the boundary is constructed, an area of the object mayeasily be calculated.

In one particular aspect, the data is compressed by evaluating only thedata which has a spectral power content below a selected fractionalthreshold. In another aspect, the object boundary and the object aredisplayed (such as on a display screen) to allow a user to indicatewhether the object boundary acceptably bounds the object. If theconstructed boundary is inaccurate or otherwise unacceptable, a newboundary may be constructed in one of two ways. In one way, the user mayselect another region of interest (e.g., by utilizing a mouse to movethe region of interest to another location on the displayed object), andrepeating steps of the method with the new region of interest.Alternatively, the data may be compressed or filtered in a differentmanner, and then repeating the steps of the method.

Typically, the ultrasound image is defined by multiple frames oftime-domain data, and the object boundary is constructed in one of theframes (conveniently referred to as a first one of the frames). Anotherone of the frames is then selected and an object boundary is constructedaround the object in the second frame and an area is calculated. Thisprocess is repeated for each frame having the object. Hence, oneadvantage of the invention is that the area of the object in subsequentframes may proceed with essentially no user interaction. Once the areashave been calculated, a volume of the object may be computed based onthe areas of the objects in the frames and the distances between theframes.

In one aspect, the object boundary around the object in the second andsubsequent frames are constructed by placing a definition region ofinterest at a center of mass of the object as determined from the first(or a previous) frame and repeating the steps that follow thedetermination of the range of acceptable object identifying parameters.

In one particularly preferable aspect, the area of the object in thefirst frame and the second frame are compared to determine if the areasdiffer by more than a predetermined amount. If so, the area of theobject in the second frame is recomputed using varied criteria. Forexample, the starting point of the definition region of interest in theobject of the second frame may be adjusted. Alternatively, the size ofthe definition region of interest may be changed. Further, the range ofacceptable object identifying parameters may be varied.

The invention will be better understood by reference to the followingdetailed description in connection with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an environment of the invention.

FIG. 2 is a depiction of an in-vivo case showing regions of interestaccording to the invention.

FIGS. 3A and 3B are together a flow chart of a process according to theinvention for adaptive computation of an object signature.

FIG. 4 is a spectrum diagram of a region of interest.

FIG. 5 is a graph of variation in an object range.

FIG. 6 is a depiction of an object definition according to theinvention.

FIG. 7 is a spectrum diagram of a region of interest showing how theobject identification parameters are representative of physicalcharacteristics of the object within a patient.

DESCRIPTION OF SPECIFIC EMBODIMENTS

The invention provides exemplary systems and methods for evaluatingobjects within ultrasonic images. The objects to be evaluated arepreferably representative of various physical features within theanatomy. Merely by way of example, such features may include tissue,plaque, blood, and the like.

The invention is particularly useful in constructing a border around theobject so that the area of the object may easily be calculated.Importantly, the invention is also useful in that it is able to modelthe object using parameters which are representative of various physicalcharacteristics of the object. These parameters are obtained fromin-vivo image data. As one example, if the physical object isconstructed at least partially from plaque, the parameters produced bythe invention convey information on the nature of the plaque, e.g. itshardness, homogeneity, and the like. In this way, the parameters may beused to more appropriately define a proscribed treatment. Further, theparameters may be saved so that each time the patient is evaluated, thesaved of parameter values may be compared to determine changes overtime.

According to the invention, the object is characterized by consideringthe in-vivo object parameters and their variability within theultrasonic imaging data. Specifically, it is assumed that each objectmay be defined in terms of statistical properties (or object identifyingparameters), which are consistently different from properties of theenvironment. Such properties are referred to as the object's signature.The statistical properties are calculated at selected locations withinthe image to determine if they fall within a predetermined range ofvalues which represents the object. If within the range, the locationsare marked to indicate they are positioned within the object. A bordermay then be drawn around the object and the area calculated.

If the border is not correctly drawn, the method is able to adjustcertain criteria and then repeat the process until convergence isobtained. Since the ultrasound data is typically stored in multiple(possibly consecutive) frames, the area of the object in each frameneeds to be computed. When computing the area of the object in asubsequent frame, a comparison is made with the previous frame todetermine if the variability in the area of the object is too great. Ifso, the invention allows the user to adjust certain criteria, or elseautomatically adjusts certain criteria to see if a better result can beobtained. Once the area in each frame is determined, a volume of theobject may be computed.

Referring now to FIG. 1, an ultrasound system 8 according to theinvention will be described. The system 8 includes a transducer 12(which is typically disposed within an imaging catheter as is known inthe art) which is driven by an exciter 10 to excite a region of interest(ROI) 14 with ultrasonic energy 16. Reflections 18 of the ultrasonicenergy are observed at a receiver 20 during a frame. Signal processingtechniques in a signal processor 22 analyze those reflections. Theinformation extracted is used to refine the excitation and observationsabout current and/or subsequent frames and to refine thecharacterization of the frame as an object model. Although not shown,system 8 preferably also includes a display screen to display each frameof data, which is typically a cross section of the image. Various entrydevices, such as keyboards, pointing devices, mice, and the like, arepreferably provided to allow the user to interact with the system. Anexemplary processor that may be used with the invention is includedwithin a Galaxy medical imaging system, commercially available fromBoston Scientific Corporation.

FIG. 2 illustrates a typical IVUS object 26 (such as plaque) in an image28 that is produced on the display screen of system 8 and represents aframe of data collected by receiver 20. As described in greater detailhereinafter, drawn onto target object 26 are two different rectangularregions of interest (ROIs) 14, 14′. ROIs 14, 14′ may be placed ontoobject 26 using one of the entry devices of the system as previouslydescribed. Moreover, although shown as being rectangular, it will beappreciated that ROIs 14 and 14′ may be of any size or geometry.Further, any number of ROIs may be employed.

A lumen 30 surround by a vessel wall 31 illustrates how the plaque 26fills the lumen 30. As is known in the art, the different objects arecharacterized by differently displayed visual intensities as well as thehomogeneity of the image. As described hereinafter, reflections fromROIs 14, 14′ preferably exhibit a spectrum differing from that of anysurrounding objects.

Referring to FIGS. 3A and 3B, a flow chart of an exemplary inventiveprocess is illustrated. The process begins by selecting a referenceframe which comprises the observed reflection signal for a time sampleof interest (Step A). Preferably, the user is allowed to select thereference frame. The selected frame is preferably the frame which bestshows object 26 (see FIG. 2). ROI 14 (see FIG. 2), which may beessentially any size or geometry, is then positioned on the desiredobject 26 (Step B). This may be accomplished, for example, by using amouse to outline ROI 14 on the display screen.

A two-dimensional fast Fourier transform (FFT) is calculated from theobserved time-domain data of ROI 14 to obtain frequency-domain data,i.e., a spectrum of the observational data in x and y (Step C). The datais then compressed by retaining only a percentage of the spectralcomponents which represent ROI 14 (Step D). Such a process isillustrated graphically in FIG. 4. As shown in the example of FIG. 4,the spectral components between f_(o) and f_(c) are kept. The valuef_(c), i.e. the amount of desired compression, is selected based on apercentage of the original area of the compressed data that is desiredto be maintained, e.g., 90% of the area under the curve of FIG. 4. Thisvalue may be varied to improve the results of the method as describedhereinafter.

Compression of the data may be accomplished, for example, by using a lowpass filter. However, it will be appreciated that various othercompression schemes may be employed. For example, the method may employa high pass filter, a band pass filter, a selective filter, and thelike. The compressed spectral components are then used to compute twokey object identification parameters (Step E). Referring to FIG. 4,these two parameters are the zero frequency magnitude AVG_(o), i.e., themagnitude of the frequency at f₀ (also referred to as the amplitude ofzero frequency), and the sum SA of the frequency magnitudes, i.e. thearea under the spectral amplitude density curve (also referred to as thespectral amplitude distribution). This area is graphically representedby the cross-hatched area under the curve of FIG. 4. As describedhereinafter, these two parameters are particularly advantageous in thatthey may be used to characterize various physical characteristics of theobject within the patient.

Next, a “definition” ROI is calculated. The definition ROI is a subsetof the originally selected ROI and is used to obtain a range ofacceptable object identification parameters. The definition ROI ispreferably selected so that is has a similar geometric shape as theoriginal ROI but with smaller dimensions. Merely by way of example, ifthe originally selected ROI were a square, and if the number ofcomponents from f_(o) to f_(max) were 256 and the number of componentsfrom f_(o) to f_(c) were 64 (which is the square root of 256), then thedimensions of the definition ROI may be the square root of 64, or 8 by 8components. As described hereinafter, the amount of compression can bevaried to enhance the results of the method, if needed. Once thedimensions of the definition ROI are determined, the definition ROI isthen reconstructed in the time domain from the compressed spectral data(Step F).

The definition ROI is then moved through the originally selected regionof interest to unique locations. At each unique location (which may beas close as pixel to pixel) a FFT is performed on the definition ROI andthe two object identifying parameters are calculated in a manner similarto the originally selected ROI. These values are then used to determinean acceptable range of object identifying parameters (Step G), sinceeach of the definition parameters belong to the originally observed ROI.This range is illustrated graphically in FIG. 5.

Returning to the original image, the definition ROI is moved to selectedlocations in the image and FFTs of the time-domain data are performed toobtain frequency-domain data for each location of the definition ROI inthe original image. From this data, the two object identificationparameters are extracted and evaluated to see if they fall within therange of FIG. 5. If so, the locations are marked or flagged to indicatethat these locations are part of the object having the originallyselected ROI.

Once all of the locations have been evaluated, a border of the object is“drawn” by the processor around the flagged locations (Step H). The areaof the object may easily be calculated simply by summing the areas ofthe flagged locations.

The user is then presented with the results (by displaying the imagewith the border on the display screen) and asked to indicate whether theborder as presented is correct or otherwise acceptable (Step I). Forexample, a window may be generated on the display screen to ask the userif the border is acceptable. A confirmation of the border is aconfirmation that the object definition is correct. If the border is notconfirmed as correct, the user is given the choice (Step J) ofoptimizing the ROI (Step K) or adding another ROI (such as ROI 14′)(Step L). The whole process (Steps B through H) is repeated for eachadded ROI. A portion of the process. (Steps D through H) is repeated ifthe ROI is to be optimized. To optimize the ROI, the amount of or typeof compression may be varied. Also, the range of acceptable objectidentifying parameters may be changed.

The foregoing process (beginning with Step L) is used for confirming theborder of a complex object and thus the definition of a complex object.The complex object is defined by the combined borders of each individualobject detected for each individual definition ROI as shown in FIG. 6.

If (in Step I) the border is confirmed, the process proceeds to the nextframe, first determining if there are more frames (Step M). If there areno more frames, the process ends (Step N). If there are still moreframes, the process proceeds to the next frame (Step O).

Referring to FIG. 3B, processing on subsequent frames proceeds withpositioning of a definition ROI (which is preferably the same definitionROI previously calculated) at a center of mass of the object (which isapproximated from the object in this previous frame) (Step P). Atwo-dimensional fast Fourier transform (FFT) is then calculated on thetime-domain data (Step Q) in order to calculate object definitionparameters (step R) and then identify the object definition parameters(step S), using the same techniques used previously. The parameters areexamined to determine if they are within the acceptable range aspreviously calculated. If so, the location of the definition ROI isflagged as defining an area belonging to the object. So long as there isan unprocessed definition ROI (Step T), the process of Steps P through Srepeats for all definition ROIs. After all definition ROIs have beenconsidered, the borders of the final object are determined and the areacontained therein is calculated (Step U) in a manner similar to thatpreviously described.

The new area value is compared with the area value computed for theprevious frame to determine whether it is within an acceptable range(Step V). If it is, the process proceeds to the next frame (Step M, FIG.3A). If not, the process enters an adaptive loop (Step W) repeatingsteps P through U) with a change of position and size of the definitionROIs or a change in the range of acceptable parameters in order toobtain an area value within an acceptable range.

If the two compared areas are substantially different from each other,there is a strong likelihood that one of the areas has been incorrectlycomputed. The loop of steps P through U provides an adaptive way tocompensate for such discrepancies. More specifically, the value of f_(c)(see FIG. 2) may be varied (or the data may be compressed in any way).Further, the starting point of the definition ROI may be moved away fromthe center of mass. Still further, the range of acceptable objectidentification parameters may be varied. In the event that convergenceis not obtained, the system may produce a message indicating that theresults did not comply with the definition.

FIG. 4 is a spectrum diagram of one ROI 14 from the average frequency f₀to a frequency beyond the maximum observed frequency f_(max). A valuef_(c) denotes the upper limit of the spectrum of the compressed values.As previously explained, the two parameters used to develop an objectdefinition are 1) the zero frequency magnitude AVG, i.e., the amplitudeat f₀, and 2) the spectral area SA, namely, the area bordered by theaxes, the compression cutoff and the amplitude-frequency plot 30. Thisplot differs with each definition ROI, as represented by plot 30′, justas the zero frequency magnitude AVG differs between amplitude 32 and32′.

FIG. 5 depicts a relationship between spectral areas SA and zerofrequency magnitudes AVG, and more particularly shows the objectdefinition range as computed in Step G (FIG. 3A). Within an object, theparameter AVG may vary between a minimum 34 and a maximum 36, and theparameter SA may vary between a minimum 38 and a maximum 40 thusestablishing the allowable parameter variations 42 for the definition ofthe object's signature. Parameters found within this range are thusidentifiable with the object.

Referring to FIG. 6, the object definition algorithm, as outlined inconnection with FIG. 3A and FIG. 3B, produces an object definition 48,e.g., for plaque, which for purposes of illustration consists of twoobjects 50 and 52. An object border 54 combines borders 56, 58 resultingfrom processing of two ROIs defining the object.

The object area is thereafter useable as a feedback parameter for theadaptive object identification algorithm as disclosed herein. The objectidentification algorithm in frames other than the reference frame (StepA) uses the results of thee previous frame to identify the object. Ifthe object area in such a frame differs more than an accepted fractionfrom the previous frame, then the adaptive mechanisms change thepositions and sizes of definition ROIs until the resultant new area iswithin an accepted fraction of the area in the previous frame. If thereis no solution to the optimization process (i.e., the solution does notconverge), then a best available approximation may be chosen as thesolution, and the border area may be denoted as uncertain.

Referring now to FIG. 7, an example of a spectrum diagram of a region ofinterest showing how the object identification parameters relate to thephysical characteristics of the object, i.e. the object within thepatient. In this example, the ultrasound image is taken within a vesselhaving a region of plaque. The AVG axis is representative of theintensity of the ultrasound image. In turn, this corresponds to thephysical composition of the actual physical image, e.g., its hardness.The f axis is representative of the spacial structure of the ultrasoundimage. In turn, this corresponds to the spacial structure, e.g.homogeneity, of the physical object. By way of example, in region 60,the actual physical object is composed of lipid plaque. In region 62,the physical object is composed of mixed plaque. In region 64, thephysical object is composed of blood, and in region 66 the physicalobject is composed of strong calcified plaque that is transitioning intotissue.

Hence, by using the f_(o) and SA values as object identificationparameters, the actual physical nature of the object may becharacterized. In this way, the methods of the invention are patientspecific and will vary from patient to patient. Moreover, the parametersmay be saved and compared with later calculated parameters to determineif a treatment is effective.

The invention has been explained with reference to specific embodiments.Other embodiments will be apparent to those of ordinary skill in theart. It is therefore not intended that this invention be limited exceptas indicated by the appended claims.

What is claimed is:
 1. A method for evaluating an object within anultrasound image, the method comprising: obtaining in-vivo ultrasoundimage data indicative of an object and the environment surrounding theobject; constructing an image from the data, wherein the image includesa depiction of the object and the surrounding environment; calculatingstatistical properties from the data at locations within the image;determining whether the statistical properties are within a certainrange that is representative of the object; and constructing a borderaround the object depicted in the image based at least in part on thedetermination.
 2. A method as in claim 1, wherein the data istime-domain data, further comprising transforming the time-domain datainto frequency-domain data and compressing the frequency-domain data,and wherein the statistical properties include parameters that comprisethe zero frequency magnitude of the compressed frequency-domain data andthe sum of the frequency magnitudes of the compressed frequency-domaindata.
 3. A method as in claim 2, wherein the object is representative ofa physical object within a patient, and wherein the zero frequencymagnitude of the compressed frequency-domain data is representative ofthe physical composition of the physical object, and the sum of thefrequency magnitudes of the compressed frequency-domain data isrepresentative of the homogeneity of the physical object.
 4. A method asin claim 2, further comprising introducing a catheter into a patient anactuating an ultrasonic element to obtain the time-domain data.
 5. Amethod for evaluating an object within the ultrasound image, the methodcomprising: obtaining in-vivo ultrasound image data in a plurality offrames; constructing images from at least some of the frames of data,wherein the images include at least one object; identifying the objectwithin each image by moving a region of interest to different locationsin the image and evaluating object identifying parameters at thedifferent locations to determine if the parameters fall within anacceptable range of object identifying parameters indicative of theobject; and computing the area of the object within each of the framesbased on the locations having the parameters which fall within theacceptable range.
 6. A method as in claim 5, further comprisingcomparing the areas of two adjacent frames, recomputing the area of oneof the adjacent frames using different criteria if the differencebetween the two areas exceeds a predetermined amount, and varying therange of object identifying parameters to recompute the area of one ofthe adjacent frames.
 7. A method as in claim 5, further comprisingvarying a starting location of the region of interest to recompute thearea of one of the adjacent frames.
 8. A method as in claim 5, furthercomprising varying the size of the region of interest to recompute thearea of one of the adjacent frames.
 9. A method as in claim 6, furthercomprising producing a message if the difference between recomputed areaand the area of the object in the adjacent frame still exceeds thepredetermined amount.
 10. An ultrasound imaging system comprising: aprocessor; a memory to store in-vivo ultrasound image data; a displayscreen coupled to the processor; code to display an image from the dataon the display screen, wherein the image includes at least one object;code to calculate with the processor statistical properties from thedata at locations within the image and to determine whether thestatistical properties are within a certain range that is representativeof the object; and code to construct an object boundary around theobject.
 11. A system as in claim 10, wherein the data is time-domaindata, further comprising code to transform with the processor thetime-domain data into frequency-domain data and to compress thefrequency-domain data, and wherein the statistical properties includeparameters that comprise the zero frequency magnitude of the compressedfrequency-domain data and the sum of the frequency magnitudes of thecompressed frequency-domain data.
 12. A system as in claim 11, whereinthe object is representative of a physical object within a patient, andwherein the zero frequency magnitude of the compressed frequency-domaindata is representative of the physical structure of the physical object,and the sum of the frequency magnitudes of the compressedfrequency-domain data is representative of the homogeneity of thephysical object.
 13. A system as in claim 11, further comprising acatheter having an ultrasonic element which is coupled to the processorto obtain the time-domain data when the catheter is inserted into apatient.
 14. An ultrasound imaging system comprising: a processor; amemory to store in-vivo ultrasound image data in a plurality of frames;a display screen coupled to the processor; code to display images fromeach frame of data on the display screen, wherein the images include atleast one object; code to identify the object within each image bymoving a region of interest to different locations in the image andevaluating object identifying parameters at the different locations todetermine if the parameters fall within an acceptable range of objectidentifying parameters indicative of the object; code to compute thearea of the object within each of the frames based on the locationshaving the parameters which fall within the acceptable range; code tocompare the areas of two adjacent frames; and code to recompute the areaof one of the adjacent frames using different criteria if the differencebetween the two areas exceeds a predetermined amount.